A large number of image processing problems are concerned with determining the structure of an anomalous area in a larger field of regard. In the context of medical imaging, there is great interest in characterizing already identified regions of interest. Image processing methods, such as computer aided diagnosis (CAD), may also be used to aid diagnosis and confirm or facilitate interpretation and findings by an image screener, such as a radiologist.
Most available CAD methods are designed to have high sensitivity. However, while having high sensitivity, most CAD systems suffer from low specificity. Thus, while these systems may highlight anomalous regions correctly (i.e., correct diagnosis or true positive), they may also incorrectly highlight healthy sections (i.e., incorrect diagnosis or false positive). Unfortunately, having low specificity may reduce the acceptance of CAD methods by the medical community since a medical professional (e.g., radiologist) may need to review the findings of a CAD module to identify false positives.